[Fugitive Dust Emission Characteristics from Building Construction Sites of Beijing]

The characteristics of PM emissions from construction sites during the earthwork and foundation stages: an empirical study evidence

The bulk of the particulate matter (PM) emissions generated during construction projects are significantly released during the earthwork and foundation stages. To reduce and control these emissions, it is necessary to have reliable data on their characteristics. However, construction PM are poorly characterized because their composition depends on several factors (e.g., weather and reduction measures) and various on-site activities whose effects may interact. To address these challenges, a long-term quantitative empirical study using advanced statistical methods was performed on a real construction project during the whole earthwork and foundation stages. The upwind-downwind method was used to collect data on PM emissions throughout the earthwork and foundation construction process, and correlation analysis, paired samples t-test, and partial least squares regression (PLS) were used to analyze TSP, PM₁₀, and PM_2.5 emissions and their relationships with various influencing factors. The results showed that both earthwork and foundation constructions generate substantial PM emissions because there were differences with statistical significances in the PM levels measured upwind and downwind of the construction site. TSP and PM₁₀ emissions correlated moderately with humidity and wind speed. However, temperature and atmospheric pressure did not correlate significantly with any of the measured emissions. The main activities responsible for PM emissions during the earthwork and foundation construction stages were hammer piling, waste stacking, and materials transportation. Water spraying was found to effectively reduce TSP and PM₁₀ emissions, while the use of a fog cannon more effectively reduced PM_2.5 emissions. Construction PM is an important source of atmospheric pollution in cities; the findings presented herein provide cornerstone and knowledge to guide efforts for reducing its impact.

Suppression Effect of Waterborne Polymer on Soil Used for Backfilling at Construction Site

To improve the dust control efficiency of soil for backfilling at construction sites, a novel waterborne polymer was used as a dust suppressant, and the dust emission model was created to control the effect of a large-scale field. The results showed that the waterborne polymer could improve the water retention efficiency of soil for backfilling, and the average water content was 2.18 times that of the watered samples, significantly delaying water evaporation. The compressive strength of soil for backfilling reached 4.91 MPa and improved the wind erosion resistance of the consolidation layer, effectively resisting wind damage. At a construction site, the waterborne polymer was sprayed on soil for backfilling, and the concentration of PM10 was reduced by 67.41%, confirming the effectiveness for large-scale utilization.

Analyzed and Simulated Prediction of Emission Characteristics of Construction Dust Particles under Multiple Pollution Sources

Dust pollution in construction sites is an invisible hazard that is often ignored as a nuisance. Regulatory and engineering control methods are predominantly used for its mitigation. To control dust, dust-generating activities and their magnitudes need to be established. While researchers have comprehensively studied dust emissions of construction work, prediction of dust concentrations based on work phases and climatic conditions is still lacking. To overcome the above knowledge gap, this article selected two construction stages of a project to monitor dust generation using the HXF-35 dust sampler. Based on the collected data, dust emission characteristics of these two stages are studied, and dust emission characteristics under multiple pollution sources are analyzed. Based on the results, a BP neural network model is built to perform simulations of dust emission concentrations in different work areas and predict construction dust concentrations under different conditions. Except few, the majority of the work areas monitored have exceeded the allowable upper limit of TSP concentration stipulated by relevant standards. In addition, dust emission differences of work areas are pronounced. The results verified that the BP neural network dust concentration prediction model is feasible to be used to predict dust concentration changes in different work faces under different climate conditions and to provide a scientific base for pollution control. This study provides several practical solutions where the prediction of dust concentrations at designated work areas will allow construction companies early warning to implement mitigation measures before it becomes a serious health hazard. In addition, it provides an opportunity to re-evaluate those hazardous work in the light of these revelations. The outcome of this study is both original and useful for both construction companies and regulatory agencies. It can better predict the concentration of construction dust in different operating areas and different weather conditions and provide a guide for the prevention and control of construction dust.

Spatial Correlation of Air Pollution and Its Causes in Northeast China

To understand the status of air pollution in northeastern China, we explore the structure of air pollution transmission networks and propose targeted policy recommendations. Using air pollution data from 35 cities in northeastern China for a total of 879 periods from 6 January 2015 to 3 June 2017, this paper used social network analysis (SNA) to construct a spatial association network of air pollution in the region, and analyzed the spatial association of air pollution among cities and its causes in an attempt to reveal the transmission path of air pollution in the region. The results show that inter-city air pollution in northeast China forms a complex and stable correlation network with obvious seasonal differences of "high in winter and low in summer". Different cities in the region play the roles of "spillover", "intermediary" and "receiver" of air pollution in the network. Small respirable particulate (PM2.5) pollution constitutes a significant component of air pollution in northeast China, which spreads from Liaoning province to Heilongjiang province via Jilin province. Therefore, regional joint pollution prevention and control measures should be adopted to combat the air pollution problem, and different treatment measures should be developed for different city "roles" in the pollution network, in order to fundamentally solve the air pollution problem in the region.

Estimating Particulate Matter Emission from Dust Sources Using ZY-3 Data and GIS Technology—A Case Study in Zhengzhou City, China

With the rapid development of the social economy in China, numerous Chinese cities are facing high levels of particulate matter (PM) pollution problems. In this study, high-resolution ZY-3 images and GIS techniques were used to establish the emission inventory of total suspended particle (TSP), particulate matter 10 (PM10) and particulate matter 2.5 (PM2.5) from fugitive dust sources in May 2016, and a spatial grid of 3 km × 3 km resolution was established to demonstrate the spatial distribution of PM emission. Results showed that the total emissions of TSP, PM10 and PM2.5 in Zhengzhou city were 237.5 kt·a−1, 103.7 kt·a−1 and 22.4 kt·a−1, respectively. Construction dust source was the main fugitive dust emission source in Zhengzhou city—the TSP, PM10 and PM2.5 emission of which account for 76.42%, 89.68% and 88.39%, respectively, of the total emission, followed by road dust source and soil dust source. PM emission was higher in Zhongyuan, Huiji, Jinshui and Zhengdong New District, while Zhongmou, Xingyang, Dengfeng and other remote areas had low PM emissions. Compared to other Chinese cities or regions, the PM emission from the construction dust source was at a high level in Zhengzhou city, while the PM emissions from the soil dust source and road dust source were at moderate levels.

PM2.5 Pollution and Inhibitory Effects on Industry Development: A Bidirectional Correlation Effect Mechanism

In this paper, a vector autoregression (VAR) model has been constructed in order to analyze a two-way mechanism between PM_2.5 pollution and industry development in Beijing via the combination of an impulse response function and variance decomposition. According to the results, long-term equilibrium interconnection was found between PM_2.5 pollution and the development of primary, secondary, and tertiary industries. One-way Granger causalities were found in the three types of industries shown to contribute to PM_2.5 pollution, though the three industries showed different scales of influences on the PM_2.5 pollution that varied for about 1–2 years. The development of the primary and secondary industries increased the emission of PM_2.5, but the tertiary industry had an inhibitory effect. In addition, PM_2.5 pollution had a certain inhibitory effect on the development of the primary and secondary industries, but the inhibition of the tertiary industry was not significant. Therefore, the development of the tertiary industry can contribute the most to the reduction of PM_2.5 pollution. Based on these findings, policy-making recommendations can be proposed regarding upcoming pollution prevention strategies.